AANP Award for Meritorious Contributions to Neuropathology Presented to Dikran S. Horoupian, MD
Bibliographic record
Abstract
Dr. Dikran Horoupian received his MD (MB, ChB) from the University of Alexandria, Egypt in 1957. Following a rotating internship, residency in general surgery, and brief tenures as an assistant attending surgeon in Egypt and a district officer of health for WHO in the Congo (now Zaire), he “saw the light” and began his training in pathology at Deer Lodge Hospital in Winnipeg, Canada. Dr. Horoupian completed his pathology training in Winnipeg General Hospital of the University of Manitoba. He then took sabbatical leave to obtain research and clinical training in neuropathology at Albert Einstein College of Medicine in New York from 1970 to 1972. Following a brief return to the University of Manitoba and a quick stop at Henry Ford Hospital, he returned to Einstein in 1975 to begin in earnest his career in academic neuropathology as the Chief of Diagnostic Neuropathology. He stayed there until his move to Stanford University as Professor of Pathology and Director of Neuropathology in 1986. He currently is Professor Emeritus at Stanford.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.313 | 0.152 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".